Keynotes
Safety Assessment of Large Generative Models
Dr. Yang Zhang
Abstract: Over the past two years, large generative models have made remarkable advancements, significantly influencing our daily lives. However, recent research highlights serious security and safety concerns associated with these models. In this talk, I will present some of our recent work addressing these challenges. First, I will discuss methods for detecting and attributing machine-generated content. Next, I will examine whether these models are likely to produce unsafe outputs. Finally, I will explore techniques for effectively extracting high-quality prompts from large models’ generated content.
Bio: Yang Zhang (https://yangzhangalmo.github.io/) is a tenured faculty member at CISPA Helmholtz Center for Information Security, Germany. His research concentrates on trustworthy machine learning including privacy, security and more recenlty LLM safety. Moreover, he works on measuring and understanding misinformation and unsafe content like hateful memes on the Internet. His research has been featured in major media outlets including the Washington Post and New Scientist. He has also received the NDSS 2019 distinguished paper award and the CCS 2022 best paper award runner-up.
TBA
Wolf Richter
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